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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Agenda\n",
      "\n",
      "- Define the problem and the approach\n",
      "- Data basics: loading data, looking at your data, basic commands\n",
      "- Handling missing values\n",
      "- Intro to scikit-learn\n",
      "- Grouping and aggregating data\n",
      "- <p style=\"color: red\">Feature selection</p>\n",
      "- Fitting and evaluating a model\n",
      "- Deploying your work"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#In this workbook you will\n",
      "- Use RandomForest to calculate the best features for your model\n",
      "- Handling outlying data using `apply` and `groupby`\n",
      "- Discretize (or bucket) data into groups\n",
      "- Make your own features for your model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"./data/credit-data-trainingset.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>serious_dlqin2yrs</th>\n",
        "      <th>revolving_utilization_of_unsecured_lines</th>\n",
        "      <th>age</th>\n",
        "      <th>number_of_time30-59_days_past_due_not_worse</th>\n",
        "      <th>debt_ratio</th>\n",
        "      <th>monthly_income</th>\n",
        "      <th>number_of_open_credit_lines_and_loans</th>\n",
        "      <th>number_of_times90_days_late</th>\n",
        "      <th>number_real_estate_loans_or_lines</th>\n",
        "      <th>number_of_time60-89_days_past_due_not_worse</th>\n",
        "      <th>number_of_dependents</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.658180</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.085113</td>\n",
        "      <td>  3042</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.233810</td>\n",
        "      <td> 30</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.036050</td>\n",
        "      <td>  3300</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.907239</td>\n",
        "      <td> 49</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.024926</td>\n",
        "      <td> 63588</td>\n",
        "      <td> 7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.754464</td>\n",
        "      <td> 39</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.209940</td>\n",
        "      <td>  3500</td>\n",
        "      <td> 8</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.189169</td>\n",
        "      <td> 57</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.606291</td>\n",
        "      <td> 23684</td>\n",
        "      <td> 9</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "   serious_dlqin2yrs  revolving_utilization_of_unsecured_lines  age  \\\n",
        "0                  0                                  0.658180   38   \n",
        "1                  0                                  0.233810   30   \n",
        "2                  0                                  0.907239   49   \n",
        "3                  0                                  0.754464   39   \n",
        "4                  0                                  0.189169   57   \n",
        "\n",
        "   number_of_time30-59_days_past_due_not_worse  debt_ratio  monthly_income  \\\n",
        "0                                            1    0.085113            3042   \n",
        "1                                            0    0.036050            3300   \n",
        "2                                            1    0.024926           63588   \n",
        "3                                            0    0.209940            3500   \n",
        "4                                            0    0.606291           23684   \n",
        "\n",
        "   number_of_open_credit_lines_and_loans  number_of_times90_days_late  \\\n",
        "0                                      2                            1   \n",
        "1                                      5                            0   \n",
        "2                                      7                            0   \n",
        "3                                      8                            0   \n",
        "4                                      9                            0   \n",
        "\n",
        "   number_real_estate_loans_or_lines  \\\n",
        "0                                  0   \n",
        "1                                  0   \n",
        "2                                  1   \n",
        "3                                  0   \n",
        "4                                  4   \n",
        "\n",
        "   number_of_time60-89_days_past_due_not_worse  number_of_dependents  \n",
        "0                                            0                     0  \n",
        "1                                            0                     0  \n",
        "2                                            0                     0  \n",
        "3                                            0                     0  \n",
        "4                                            0                     2  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Finding Important Features\n",
      "We're going to let scikit-learn help us determine which variables are the best at predicting risk. To do this, we're going to use an algorithm called `RandomForest`. `RandomForest` randomly generates a \"forest\" of decision trees. As the trees are randomly generated, the algorithm takes turns leaving out each variable in fitting the model. This allows the `RandomForest` to calculate just how much worse a model does when each variable is left out."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestClassifier\n",
      "\n",
      "features = np.array(['revolving_utilization_of_unsecured_lines',\n",
      "                     'age', 'number_of_time30-59_days_past_due_not_worse',\n",
      "                     'debt_ratio', 'monthly_income','number_of_open_credit_lines_and_loans', \n",
      "                     'number_of_times90_days_late', 'number_real_estate_loans_or_lines',\n",
      "                     'number_of_time60-89_days_past_due_not_worse', 'number_of_dependents'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = RandomForestClassifier(compute_importances=True)\n",
      "clf.fit(df[features], df['serious_dlqin2yrs'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/ensemble/forest.py:783: DeprecationWarning: Setting compute_importances is no longer required as version 0.14. Variable importances are now computed on the fly when accessing the feature_importances_ attribute. This parameter will be removed in 0.16.\n",
        "  DeprecationWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
        "            criterion='gini', max_depth=None, max_features='auto',\n",
        "            min_density=None, min_samples_leaf=1, min_samples_split=2,\n",
        "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
        "            verbose=0)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# from the calculated importances, order them from most to least important\n",
      "# and make a barplot so we can visualize what is/isn't important\n",
      "importances = clf.feature_importances_\n",
      "sorted_idx = np.argsort(importances)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding = np.arange(len(features)) + 0.5\n",
      "pl.barh(padding, importances[sorted_idx], align='center')\n",
      "pl.yticks(padding, features[sorted_idx])\n",
      "pl.xlabel(\"Relative Importance\")\n",
      "pl.title(\"Variable Importance\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2wmg0IiEhATKZDP7+/lAqlRMaszXW8mDvc2ctP0RE94tTp04BGKodJmPZVdz2AF6lUono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\nIyIiIgoS/w8iDCcJB302XAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1094a7250>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So you can see that the best variable is `revolving_utilization_of_unsecured_lines` while the worst is `number_real_estate_loans_or_lines`. There's also a dramatic drop off after `number_of_open_credit_lines_and_loans`. This is where you need to use your own discretion. How many variables should you include in your model?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Engineering Additional Features\n",
      "Feature selection/engineering will likely have the biggest impact in determining the success/failure of your model. Even if you're using the latest and greatest algorithm, if you put in non-important features you're going to get poor results. Remember, it's math, not magic.\n",
      "\n",
      "Feature engineering is a skill that will take time to get the hang of. Sometimes the best way is to just talk to people. Ask questions, brainstorm with others, etc. Oftentimes 2 features might not be helpful when used individually, but when combined they can be extremely powerful.\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['income_bins'] = pd.cut(df.monthly_income, bins=15)\n",
      "pd.value_counts(df['income_bins'])\n",
      "# not very helpful"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(-1794.06, 119604]    112875\n",
        "(119604, 239208]          28\n",
        "(239208, 358812]           4\n",
        "(598020, 717624]           4\n",
        "(717624, 837228]           2\n",
        "(358812, 478416]           2\n",
        "(1674456, 1794060]         1\n",
        "(478416, 598020]           1\n",
        "(1554852, 1674456]         1\n",
        "(956832, 1076436]          1"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Bucketing Continuous Values"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Use the cap_value function you wrote previously to cap monthly_income at $15,000"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cap_values(x, cap):\n",
      "    if x > cap:\n",
      "        return cap\n",
      "    else:\n",
      "        return x\n",
      "    \n",
      "df.monthly_income = df.monthly_income.apply(lambda x: cap_values(x, 15000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.monthly_income.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "count    112919.000000\n",
        "mean       4993.724165\n",
        "std        4076.537284\n",
        "min           0.000000\n",
        "25%        1868.500000\n",
        "50%        4416.000000\n",
        "75%        7416.000000\n",
        "max       15000.000000"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['income_bins'] = pd.cut(df.monthly_income, bins=15, labels=False)\n",
      "pd.value_counts(df.income_bins)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0     23991\n",
        "4     12000\n",
        "3     11918\n",
        "2     10459\n",
        "5     10095\n",
        "6      8127\n",
        "7      6841\n",
        "1      5694\n",
        "8      5329\n",
        "14     5108\n",
        "9      4549\n",
        "10     3475\n",
        "11     2292\n",
        "12     1835\n",
        "13     1206"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[[\"income_bins\", \"serious_dlqin2yrs\"]].groupby(\"income_bins\").mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>serious_dlqin2yrs</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>income_bins</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 0.055646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 0.104320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 0.091978</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 0.085669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 0.075833</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 0.069638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 0.063123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 0.058763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 0.053481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 0.051000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 0.039424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 0.039267</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 0.040327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 0.038143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 0.045810</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "             serious_dlqin2yrs\n",
        "income_bins                   \n",
        "0                     0.055646\n",
        "1                     0.104320\n",
        "2                     0.091978\n",
        "3                     0.085669\n",
        "4                     0.075833\n",
        "5                     0.069638\n",
        "6                     0.063123\n",
        "7                     0.058763\n",
        "8                     0.053481\n",
        "9                     0.051000\n",
        "10                    0.039424\n",
        "11                    0.039267\n",
        "12                    0.040327\n",
        "13                    0.038143\n",
        "14                    0.045810"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cols = [\"income_bins\", \"serious_dlqin2yrs\"]\n",
      "df[cols].groupby(\"income_bins\").mean().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1094aced0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pdJDL5ZDL5dBoNDAajQ63NRgMsNlsKC4udjj0AaCQd+xSF+oXrsQzk/viiUkJ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bdfd9d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cols = ['age', 'serious_dlqin2yrs']\n",
      "age_means = df[cols].groupby(\"age\").mean()\n",
      "age_means.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10be5ae90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rgFDX+oPA1q1bsWbNGkyePDnZ4yciVV6gRJ5ShoMtFlw6mrrIkpHn5QVImSxf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\n4jAFAUKSyKhjYXMHt6S2uTl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BhxMSOl0tndBM4CJt374dzz//fKBY6S983njjjWhra8Py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\nJItRECCEkCxGQYAQQrIYBQFCCMli/x+zfCNRUmfXYgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10be5e850>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Bin `age` into 20 different groups. Then make a frequency table that shows the number of customers that were/were not delinquent for each bin.\n",
      "*HINT: You might want to have larger bins near the min/max values to account for outliers.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#DELETE THIS\n",
      "#df[\"age_bucket\"] = pd.cut(df.age, range(-1, 110, 10))\n",
      "#buckets = [-1, 25] + range(25, 80, 5) + [80, 120]\n",
      "#df[\"age_bucket\"] = pd.cut(df.age, buckets) \n",
      "#pd.crosstab(df.age_bucket, df.serious_dlqin2yrs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>serious_dlqin2yrs</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>age_bucket</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>(-1, 25]</th>\n",
        "      <td>  2023</td>\n",
        "      <td>  248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(25, 30]</th>\n",
        "      <td>  5157</td>\n",
        "      <td>  672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(30, 35]</th>\n",
        "      <td>  7247</td>\n",
        "      <td>  883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(35, 40]</th>\n",
        "      <td>  9281</td>\n",
        "      <td>  937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(40, 45]</th>\n",
        "      <td> 11182</td>\n",
        "      <td> 1043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(45, 50]</th>\n",
        "      <td> 13005</td>\n",
        "      <td> 1124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(50, 55]</th>\n",
        "      <td> 12531</td>\n",
        "      <td>  959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(55, 60]</th>\n",
        "      <td> 12077</td>\n",
        "      <td>  652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(60, 65]</th>\n",
        "      <td> 11880</td>\n",
        "      <td>  497</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(65, 70]</th>\n",
        "      <td>  8003</td>\n",
        "      <td>  228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(70, 75]</th>\n",
        "      <td>  5525</td>\n",
        "      <td>  143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(75, 80]</th>\n",
        "      <td>  3840</td>\n",
        "      <td>   82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(80, 120]</th>\n",
        "      <td>  3630</td>\n",
        "      <td>   70</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "serious_dlqin2yrs      0     1\n",
        "age_bucket                    \n",
        "(-1, 25]            2023   248\n",
        "(25, 30]            5157   672\n",
        "(30, 35]            7247   883\n",
        "(35, 40]            9281   937\n",
        "(40, 45]           11182  1043\n",
        "(45, 50]           13005  1124\n",
        "(50, 55]           12531   959\n",
        "(55, 60]           12077   652\n",
        "(60, 65]           11880   497\n",
        "(65, 70]            8003   228\n",
        "(70, 75]            5525   143\n",
        "(75, 80]            3840    82\n",
        "(80, 120]           3630    70"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Using the age bins, calculate the percent of customers that were delinquent for each bucket"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#DELETE#df[[\"age_bucket\", \"serious_dlqin2yrs\"]].groupby(\"age_bucket\").mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>serious_dlqin2yrs</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>age_bucket</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>(-1, 25]</th>\n",
        "      <td> 0.109203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(25, 30]</th>\n",
        "      <td> 0.115286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(30, 35]</th>\n",
        "      <td> 0.108610</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(35, 40]</th>\n",
        "      <td> 0.091701</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(40, 45]</th>\n",
        "      <td> 0.085317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(45, 50]</th>\n",
        "      <td> 0.079553</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(50, 55]</th>\n",
        "      <td> 0.071090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(55, 60]</th>\n",
        "      <td> 0.051222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(60, 65]</th>\n",
        "      <td> 0.040155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(65, 70]</th>\n",
        "      <td> 0.027700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(70, 75]</th>\n",
        "      <td> 0.025229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(75, 80]</th>\n",
        "      <td> 0.020908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(80, 120]</th>\n",
        "      <td> 0.018919</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "            serious_dlqin2yrs\n",
        "age_bucket                   \n",
        "(-1, 25]             0.109203\n",
        "(25, 30]             0.115286\n",
        "(30, 35]             0.108610\n",
        "(35, 40]             0.091701\n",
        "(40, 45]             0.085317\n",
        "(45, 50]             0.079553\n",
        "(50, 55]             0.071090\n",
        "(55, 60]             0.051222\n",
        "(60, 65]             0.040155\n",
        "(65, 70]             0.027700\n",
        "(70, 75]             0.025229\n",
        "(75, 80]             0.020908\n",
        "(80, 120]            0.018919"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[[\"age_bucket\", \"serious_dlqin2yrs\"]].groupby(\"age_bucket\").mean().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10be13a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Iwe7r540IXyX+vvcXHCnUY8noUKjl3ONNtrG6m0un00Gr1UImk0EmkyEgIAA6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1068d7f10>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels, levels = pd.factorize(df.age_bucket)\n",
      "df.age_bucket = labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Write something that buckets debt_ratio into 4 (nearly) equally sized groups.\n",
      "*Hint: use the `quantile` method for Series*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#DELETE\n",
      "#bins = []\n",
      "#for q in [0, .25, .5, .75, 1]:\n",
      "#    bins.append(df.debt_ratio.quantile(q))\n",
      "\n",
      "debt_ratio_binned = pd.cut(df.debt_ratio, bins=bins)\n",
      "\n",
      "print pd.value_counts(debt_ratio_binned)[1]==37500"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Scaling Features\n",
      "Some algorithms will work better if your data is centered around 0. The `StandardScaler` module in `scikit-learn` makes it very easy to quickly scale columns in your data frame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import StandardScaler\n",
      "\n",
      "df['monthly_income_scaled'] = StandardScaler().fit_transform(df.monthly_income)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.monthly_income_scaled.describe()\n",
      "print\n",
      "print \"Mean at 0?\", round(df.monthly_income_scaled.mean(), 10)==0\n",
      "\n",
      "pl.hist(df.monthly_income_scaled)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "count    1.129190e+05\n",
        "mean     4.520662e-14\n",
        "std      1.000004e+00\n",
        "min     -1.224997e+00\n",
        "25%     -7.666404e-01\n",
        "50%     -1.417200e-01\n",
        "75%      5.942020e-01\n",
        "max      2.454613e+00\n",
        "\n",
        "Mean at 0? True\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(array([25826, 14318, 17677, 16336, 11667,  8630,  6635,  3681,  2359,  5790]),\n",
        " array([-1.22499708, -0.8570361 , -0.48907513, -0.12111415,  0.24684683,\n",
        "        0.6148078 ,  0.98276878,  1.35072976,  1.71869073,  2.08665171,\n",
        "        2.45461269]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10726f7d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Redo our feature importance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = np.array(['revolving_utilization_of_unsecured_lines',\n",
      "                     'age', 'number_of_time30-59_days_past_due_not_worse',\n",
      "                     'debt_ratio', 'monthly_income','number_of_open_credit_lines_and_loans', \n",
      "                     'number_of_times90_days_late', 'number_real_estate_loans_or_lines',\n",
      "                     'number_of_time60-89_days_past_due_not_worse', 'number_of_dependents',\n",
      "                     'income_bins', 'age_bucket', 'monthly_income_scaled'])\n",
      "\n",
      "clf = RandomForestClassifier(compute_importances=True)\n",
      "clf.fit(df[features], df['serious_dlqin2yrs'])\n",
      "\n",
      "importances = clf.feature_importances_\n",
      "sorted_idx = np.argsort(importances)\n",
      "\n",
      "padding = np.arange(len(features)) + 0.5\n",
      "pl.barh(padding, importances[sorted_idx], align='center')\n",
      "pl.yticks(padding, features[sorted_idx])\n",
      "pl.xlabel(\"Relative Importance\")\n",
      "pl.title(\"Variable Importance\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/ensemble/forest.py:783: DeprecationWarning: Setting compute_importances is no longer required as version 0.14. Variable importances are now computed on the fly when accessing the feature_importances_ attribute. This parameter will be removed in 0.16.\n",
        "  DeprecationWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nC+Tm5pKbmzvs9OOt/b1w4QLx8fEe+3X8+HGsVivR0dFaG0ajkaeeeopXXnmF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\nCCGE0Agp3IQQQgghNEIKNyGEEEIIjfg/3WWXz0dCT4MAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107280210>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "best_features = features[sorted_idx][::-1]\n",
      "best_features"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array(['revolving_utilization_of_unsecured_lines', 'debt_ratio',\n",
        "       'monthly_income', 'number_of_times90_days_late', 'age',\n",
        "       'monthly_income_scaled', 'number_of_open_credit_lines_and_loans',\n",
        "       'number_of_time30-59_days_past_due_not_worse',\n",
        "       'number_of_time60-89_days_past_due_not_worse', 'age_bucket',\n",
        "       'number_of_dependents', 'number_real_estate_loans_or_lines',\n",
        "       'income_bins'], \n",
        "      dtype='|S43')"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Test out your own features\n",
      "Partner with the person sitting next to you and see if you can come up with some new features that outperform the basic variables.\n",
      "\n",
      "- brainstorm new features\n",
      "- create them using pandas\n",
      "- test their effectiveness w/ scikit-learn\n",
      "\n",
      "Things you might try:\n",
      "\n",
      "- Try using some of the scikit-learn [preprecessing functions](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing)\n",
      "- Use `pd.get_dummies` for making numerical variables into categorical variables\n",
      "- Combine features together (total times late, log(monthly_income), age^2)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##We just did the following\n",
      "\n",
      "- Ranked feature importance using RandomForest\n",
      "- Created some of our own features using `pandas` and `scikit-learn`\n",
      "- Scaled data using `scikit-learn`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##Eventually delete the remainder"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = best_features[:5]\n",
      "df[features].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>revolving_utilization_of_unsecured_lines</th>\n",
        "      <th>debt_ratio</th>\n",
        "      <th>monthly_income</th>\n",
        "      <th>monthly_income_scaled</th>\n",
        "      <th>age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 150000.000000</td>\n",
        "      <td> 150000.000000</td>\n",
        "      <td> 1.500000e+05</td>\n",
        "      <td> 1.500000e+05</td>\n",
        "      <td> 150000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>      6.048438</td>\n",
        "      <td>    353.005076</td>\n",
        "      <td> 5.547716e-14</td>\n",
        "      <td> 5.547716e-14</td>\n",
        "      <td>     52.295207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    249.755371</td>\n",
        "      <td>   2037.818523</td>\n",
        "      <td> 1.000003e+00</td>\n",
        "      <td> 1.000003e+00</td>\n",
        "      <td>     14.771866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>-1.222482e+00</td>\n",
        "      <td>-1.222482e+00</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.029867</td>\n",
        "      <td>      0.175074</td>\n",
        "      <td>-7.819155e-01</td>\n",
        "      <td>-7.819155e-01</td>\n",
        "      <td>     41.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>      0.154181</td>\n",
        "      <td>      0.366508</td>\n",
        "      <td>-1.364860e-01</td>\n",
        "      <td>-1.364860e-01</td>\n",
        "      <td>     52.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>      0.559046</td>\n",
        "      <td>      0.868254</td>\n",
        "      <td> 5.973016e-01</td>\n",
        "      <td> 5.973016e-01</td>\n",
        "      <td>     63.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  50708.000000</td>\n",
        "      <td> 329664.000000</td>\n",
        "      <td> 2.448904e+00</td>\n",
        "      <td> 2.448904e+00</td>\n",
        "      <td>    109.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 249,
       "text": [
        "       revolving_utilization_of_unsecured_lines     debt_ratio  monthly_income  \\\n",
        "count                             150000.000000  150000.000000    1.500000e+05   \n",
        "mean                                   6.048438     353.005076    5.547716e-14   \n",
        "std                                  249.755371    2037.818523    1.000003e+00   \n",
        "min                                    0.000000       0.000000   -1.222482e+00   \n",
        "25%                                    0.029867       0.175074   -7.819155e-01   \n",
        "50%                                    0.154181       0.366508   -1.364860e-01   \n",
        "75%                                    0.559046       0.868254    5.973016e-01   \n",
        "max                                50708.000000  329664.000000    2.448904e+00   \n",
        "\n",
        "       monthly_income_scaled            age  \n",
        "count           1.500000e+05  150000.000000  \n",
        "mean            5.547716e-14      52.295207  \n",
        "std             1.000003e+00      14.771866  \n",
        "min            -1.222482e+00       0.000000  \n",
        "25%            -7.819155e-01      41.000000  \n",
        "50%            -1.364860e-01      52.000000  \n",
        "75%             5.973016e-01      63.000000  \n",
        "max             2.448904e+00     109.000000  "
       ]
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cap_outliers(x):\n",
      "    mask = (x >= x.quantile(.95))\n",
      "    x[mask] = x.median()\n",
      "    return x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 250
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.debt_ratio = cap_outliers(df.debt_ratio)\n",
      "df.monthly_income = cap_outliers(df.monthly_income)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 251
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[features].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>revolving_utilization_of_unsecured_lines</th>\n",
        "      <th>debt_ratio</th>\n",
        "      <th>monthly_income</th>\n",
        "      <th>monthly_income_scaled</th>\n",
        "      <th>age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 150000.000000</td>\n",
        "      <td> 150000.000000</td>\n",
        "      <td> 150000.000000</td>\n",
        "      <td> 1.500000e+05</td>\n",
        "      <td> 150000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>      6.048438</td>\n",
        "      <td>    132.187917</td>\n",
        "      <td>     -0.127024</td>\n",
        "      <td> 5.547716e-14</td>\n",
        "      <td>     52.295207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    249.755371</td>\n",
        "      <td>    431.584439</td>\n",
        "      <td>      0.833931</td>\n",
        "      <td> 1.000003e+00</td>\n",
        "      <td>     14.771866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>     -1.222482</td>\n",
        "      <td>-1.222482e+00</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.029867</td>\n",
        "      <td>      0.175074</td>\n",
        "      <td>     -0.781916</td>\n",
        "      <td>-7.819155e-01</td>\n",
        "      <td>     41.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>      0.154181</td>\n",
        "      <td>      0.366506</td>\n",
        "      <td>     -0.136486</td>\n",
        "      <td>-1.364860e-01</td>\n",
        "      <td>     52.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>      0.559046</td>\n",
        "      <td>      0.649179</td>\n",
        "      <td>      0.409082</td>\n",
        "      <td> 5.973016e-01</td>\n",
        "      <td>     63.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  50708.000000</td>\n",
        "      <td>   2448.000000</td>\n",
        "      <td>      2.101835</td>\n",
        "      <td> 2.448904e+00</td>\n",
        "      <td>    109.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 252,
       "text": [
        "       revolving_utilization_of_unsecured_lines     debt_ratio  monthly_income  \\\n",
        "count                             150000.000000  150000.000000   150000.000000   \n",
        "mean                                   6.048438     132.187917       -0.127024   \n",
        "std                                  249.755371     431.584439        0.833931   \n",
        "min                                    0.000000       0.000000       -1.222482   \n",
        "25%                                    0.029867       0.175074       -0.781916   \n",
        "50%                                    0.154181       0.366506       -0.136486   \n",
        "75%                                    0.559046       0.649179        0.409082   \n",
        "max                                50708.000000    2448.000000        2.101835   \n",
        "\n",
        "       monthly_income_scaled            age  \n",
        "count           1.500000e+05  150000.000000  \n",
        "mean            5.547716e-14      52.295207  \n",
        "std             1.000003e+00      14.771866  \n",
        "min            -1.222482e+00       0.000000  \n",
        "25%            -7.819155e-01      41.000000  \n",
        "50%            -1.364860e-01      52.000000  \n",
        "75%             5.973016e-01      63.000000  \n",
        "max             2.448904e+00     109.000000  "
       ]
      }
     ],
     "prompt_number": 252
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import RandomizedPCA\n",
      "from sklearn.preprocessing import StandardScaler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 253
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = RandomizedPCA(n_components=2)\n",
      "for f in features:\n",
      "    df[f] = StandardScaler().fit_transform(df[f].astype(np.float))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 254
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = pca.fit_transform(df[features])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 256,
       "text": [
        "array([[ 1.64476218, -0.35298587],\n",
        "       [-0.57412757, -0.85206089],\n",
        "       [-0.4217929 , -0.96200735],\n",
        "       ..., \n",
        "       [-1.52065449,  1.79339764],\n",
        "       [-1.4689103 , -0.3112598 ],\n",
        "       [-1.47520656,  0.18290261]])"
       ]
      }
     ],
     "prompt_number": 256
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.scatter(X[::,0], X[::,1], c=df.serious_dlqin2yrs)\n",
      "#barf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 259,
       "text": [
        "<matplotlib.collections.PathCollection at 0x119f04210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D2TlTpqRhtbqxWkRIxD5WrpxwLn5t5fjxYNhJJAqmkm7eXEZKip7aWhstLcEw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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118c74290>"
       ]
      }
     ],
     "prompt_number": 259
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import MiniBatchKMeans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km = MiniBatchKMeans(n_clusters=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 295
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.fit(df[features])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 296,
       "text": [
        "MiniBatchKMeans(batch_size=100, compute_labels=True, init='k-means++',\n",
        "        init_size=None, k=None, max_iter=100, max_no_improvement=10,\n",
        "        n_clusters=20, n_init=3, random_state=None,\n",
        "        reassignment_ratio=0.01, tol=0.0, verbose=0)"
       ]
      }
     ],
     "prompt_number": 296
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clusters = pca.fit_transform(km.cluster_centers_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 297
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['cluster'] = km.predict(df[features])\n",
      "pd.value_counts(df.cluster)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 298,
       "text": [
        "4     17865\n",
        "0     14241\n",
        "8     13997\n",
        "3     13852\n",
        "2     11809\n",
        "15    10462\n",
        "14     9819\n",
        "9      9363\n",
        "11     9080\n",
        "16     8534\n",
        "18     6294\n",
        "19     3974\n",
        "6      3933\n",
        "10     3516\n",
        "13     3090\n",
        "12     2944\n",
        "7      2917\n",
        "1      2301\n",
        "17     1947\n",
        "5        62"
       ]
      }
     ],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cluster_risk = df.groupby('cluster').apply(lambda x: x.serious_dlqin2yrs.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.scatter(clusters[::,0], clusters[::,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 300,
       "text": [
        "<matplotlib.collections.PathCollection at 0x119f9cc10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FRy5J//hHVL/9bT95njR7druuu47b2AIIh7zoyI8fl77xjRJt3nzmPd6qqjr1yist3M4W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\nyAHAOAY5ABjHIAcA4xjkAGAcgxwAjPt/In0EOvw3H3UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b24c850>"
       ]
      }
     ],
     "prompt_number": 300
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for cluster, color in zip(clusters, cluster_risk.values):\n",
      "    pl.scatter(cluster[0], cluster[1], s=color*1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x119fab190>"
       ]
      }
     ],
     "prompt_number": 301
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 278,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 278
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}